Algorithmic Invention

In his 1966 essay “Rhétorique et enseignement,” Gérard Genette observes
that literary studies did not always emphasize the reading of texts.
Before the end of the nineteenth century, the study of literature
revolved around the art of writing. Texts were not objects to interpret
but models to imitate. The study of literature emphasized elocutio, or
style and the arrangement of words. With the rise of literary history,
academic reading approached texts as objects to be explained. Students
learned to read in order to write essays (dissertations) where they
analyzed texts according to prescribed methods. This new way of studying
literature stressed dispositio, or the organization of ideas. Recent
developments in information technology have further challenged paradigms
for reading literature. Digital tools and resources allow for the study
of large collections of texts using quantitative methods. Various
computational methods of distant as well as close reading facilitate
investigations into fundamental questions of the possibilities for
literary creation. Technology has the potential for exploring inventio,
or the finding of ideas that can be expressed through writing.

One possibility is the Word Vector Topic Generator
(https://github.com/mbwolff/WVTG), a Python script that makes use of
vector space models of words. These models represent relationships
between words from a defined corpus in spatial terms and can be used to
calculate semantic similarities and differences. With a corpus and a
given text it is possible to generate a new text according to how
language was used within the corpus. Considered as an algorithmic topos
in the Aristotelian sense, the WVTG instantiates an opposition to the
thesis of an asserted text through analogy. Three inputs are required:
the asserted text, the corpus from which a vector space model of words
is derived, and a pair of words establishing an analogy for
substitutions in the text. For instance, a corpus of 117 texts by Honoré
de Balzac produces a vector space model of words that can generate a
new text from Charles Baudelaire’s prose poem Enivrez-vous! by replacing
each word in the poem with a word in the vector space model that best
completes an analogy from the opposition bénir/maudire as expressed in
Balzac’s writing. The code allows a user to easily experiment with
different corpora and analogies to generate different texts.

Unlike a traditional notion of invention positing that arguments to
persuade an audience are discoverable within a shared and uncontested
discursive space, the algorithmic invention of WVTG parameterizes both
the discursive space and the relationships between words. Rhetorical
invention such as this explores the potentiality of language as members
of the Oulipo have done with techniques such as Jean Lescure’s S+7
method, Marcel Bénabou’s aphorism formulas and the ALAMO’s rimbaudelaire
poems. The WVTG implements analytical tools from the digital humanities
as a means for creating e-literature. With technology we can explore
not only how something was written and why it was written, but also what
was possible to write given a historical linguistic context.